class_docstr: Approximate Large Margin Algorithm (ALMA).
  
  Parameters
  ----------
  p
  alpha
  B
  C
  
  Attributes
  ----------
  w : collections.defaultdict
      The current weights.
  k : int
      The number of instances seen during training.
  
  Examples
  --------
  
  >>> from river import datasets
  >>> from river import evaluate
  >>> from river import linear_model
  >>> from river import metrics
  >>> from river import preprocessing
  
  >>> dataset = datasets.Phishing()
  
  >>> model = (
  ...     preprocessing.StandardScaler() |
  ...     linear_model.ALMAClassifier()
  ... )
  
  >>> metric = metrics.Accuracy()
  
  >>> evaluate.progressive_val_score(dataset, model, metric)
  Accuracy: 82.56%
  
  References
  ----------
  [^1]: [Gentile, Claudio. "A new approximate maximal margin classification algorithm." Journal of Machine Learning Research 2.Dec (2001): 213-242](http://www.jmlr.org/papers/volume2/gentile01a/gentile01a.pdf)
code: def _raw_dot(self, x):
      return utils.math.dot(x, self.w)
  
file_path: river/river/linear_model/alma.py
imports: from __future__ import annotations
  import collections
  import math
  from river import base, utils
label_desc: Use image node_id 2 for calling the ALMAClassifier obj's underlying member method code with example usage: obj._raw_dot(x) and returns: utils